[1]} employs the normalization with LayerScale, which uses learnable factors to adaptively normalize the features. The normalization speeds up the converge rate and allows the deep model to be trained well. CaiT also includes Class Attention Layer for computing the attention between the class embedding and the overall features to have better knowledge on the inputs.
